Fulvestrant (Faslodex™) is a pure antiestrogen that is approved to treat hormone receptor-positive metastatic breast cancer in postmenopausal women. Previous studies have demonstrated that fulvestrant metabolism in humans involves cytochromes P450 and UDP-glucuronosyltransferases (UGTs). To date, fulvestrant sulfation has not been characterized. This study examined fulvestrant sulfation with nine recombinant sulfotransferases and found that only SULT1A1 and SULT1E1 displayed catalytic activity toward this substrate, with K(m) of 4.2 ± 0.99 and 0.2 ± 0.16 μM, respectively. In vitro assays of 104 human liver cytosols revealed marked individual variability that was highly correlated with β-naphthol sulfation (SULT1A1 diagnostic substrate; r = 0.98, P < 0.0001), but not with 17β-estradiol sulfation (SULT1E1 diagnostic substrate; r = 0.16, P = 0.10). Fulvestrant sulfation was correlated with both SULT1A1*1/2 genotype (P value = 0.023) and copy number (P < 0.0001). These studies suggest that factors influencing SULT1A1/1E1 tissue expression and/or enzymatic activity could influence the efficacy of fulvestrant therapy.

Gene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.

The discovery of vitamins and clarification of their role in preventing frank essential nutrient deficiencies occurred in the early 1900s. Much vitamin research has understandably focused on public health and the effects of single nutrients to alleviate acute conditions. The physiological processes for maintaining health, however, are complex systems that depend upon interactions between multiple nutrients, environmental factors, and genetic makeup. To analyze the relationship between these factors and nutritional health, data were obtained from an observational, community-based participatory research program of children and teens (age 6-14) enrolled in a summer day camp in the Delta region of Arkansas. Assessments of erythrocyte S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), plasma homocysteine (Hcy) and 6 organic micronutrients (retinol, 25-hydroxy vitamin D3, pyridoxal, thiamin, riboflavin, and vitamin E), and 1,129 plasma proteins were performed at 3 time points in each of 2 years. Genetic makeup was analyzed with 1 M SNP genotyping arrays, and nutrient status was assessed with 24-h dietary intake questionnaires. A pattern of metabolites (met_PC1) that included the ratio of erythrocyte SAM/SAH, Hcy, and 5 vitamins were identified by principal component analysis. Met_PC1 levels were significantly associated with (1) single-nucleotide polymorphisms, (2) levels of plasma proteins, and (3) multilocus genotypes coding for gastrointestinal and immune functions, as identified in a global network of metabolic/protein-protein interactions. Subsequent mining of data from curated pathway, network, and genome-wide association studies identified genetic and functional relationships that may be explained by gene-nutrient interactions. The systems nutrition strategy described here has thus associated a multivariate metabolite pattern in blood with genes involved in immune and gastrointestinal functions.

Recent advances in high-throughput genotyping technology are paving the way for research in personalized medicine and nutrition. However, most of the genetic markers identified from association studies account for a small contribution to the total risk/benefit of the studied phenotypic trait. Testing whether the candidate genes identified by association studies are causal is critically important to the development of personalized medicine and nutrition. An efficient data mining strategy and a set of sophisticated tools are necessary to help better understand and utilize the findings from genetic association studies.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.

Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs) in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders' quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.

Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

The mechanisms underlying T-ALL relapse remain essentially unknown. Multilevel-omics in 38 matched pairs of initial and relapsed T-ALL revealed 18 (47%) type-1 (defined by being derived from the major ancestral clone) and 20 (53%) type-2 relapses (derived from a minor ancestral clone). In both types of relapse, we observed known and novel drivers of multidrug resistance including MDR1 and MVP, NT5C2 and JAK-STAT activators. Patients with type-1 relapses were specifically characterized by IL7R upregulation. In remarkable contrast, type-2 relapses demonstrated (1) enrichment of constitutional cancer predisposition gene mutations, (2) divergent genetic and epigenetic remodeling, and (3) enrichment of somatic hypermutator phenotypes, related to BLM, BUB1B/PMS2 and TP53 mutations. T-ALLs that later progressed to type-2 relapses exhibited a complex subclonal architecture, unexpectedly, already at the time of initial diagnosis. Deconvolution analysis of ATAC-Seq profiles showed that T-ALLs later developing into type-1 relapses resembled a predominant immature thymic T-cell population, whereas T-ALLs developing into type-2 relapses resembled a mixture of normal T-cell precursors. In sum, our analyses revealed fundamentally different mechanisms driving either type-1 or type-2 T-ALL relapse and indicate that differential capacities of disease evolution are already inherent to the molecular setup of the initial leukemia.

Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.

Published studies have identified genetic variants, somatic mutations, and changes in gene expression profiles that are associated with hepatocellular carcinoma (HCC), particularly involving genes that encode drug metabolizing enzymes (DMEs). CYP2C9, one of the most abundant and important DMEs, is involved in the metabolism of many carcinogens and drugs and is down-regulated in HCC. To investigate the molecular mechanisms that control CYP2C9 expression, we applied integrative approaches including in silico, in vitro, and in vivo analyses to elucidate the role of microRNA hsa-miR-128-3p in the regulation of CYP2C9 expression and translation. RNA electrophoresis mobility shift assays demonstrated a direct interaction between hsa-miR-128-3p and its cognate target, the CYP2C9 transcript. Furthermore, the expression of a luciferase reporter gene containing the 3'-UTR of CYP2C9 and the endogenous expression of CYP2C9 were suppressed by transfection of hsa-miR-128-3p. Importantly, chemically-induced up- or down-regulation of hsa-miR-128-3p correlated inversely with the expression of CYP2C9. Finally, an association analysis revealed that the expression of hsa-miR-128-3p is inversely correlated with the expression of CYP2C9 in HCC tumor tissues. Altogether, the study helped to elucidate the mechanism of CYP2C9 regulation by hsa-miR-128-3p, and the inverse association in HCC.

The rat has been used extensively as a model for evaluating chemical toxicities and for understanding drug mechanisms. However, its transcriptome across multiple organs, or developmental stages, has not yet been reported. Here we show, as part of the SEQC consortium efforts, a comprehensive rat transcriptomic BodyMap created by performing RNA-Seq on 320 samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats. We catalogue the expression profiles of 40,064 genes, 65,167 transcripts, 31,909 alternatively spliced transcript variants and 2,367 non-coding genes/non-coding RNAs (ncRNAs) annotated in AceView. We find that organ-enriched, differentially expressed genes reflect the known organ-specific biological activities. A large number of transcripts show organ-specific, age-dependent or sex-specific differential expression patterns. We create a web-based, open-access rat BodyMap database of expression profiles with crosslinks to other widely used databases, anticipating that it will serve as a primary resource for biomedical research using the rat model.

The objective of this study was to determine copy number variant (CNV) and promoter genetic variants in glutathione S-transferase Mu class 1 (GSTM1) and the risk of recurrence (REC)/second primary tumor (SPT) in patients with previously diagnosed early stage head and neck cancer. Among 441 subjects, 133 experienced REC and/or an SPT, while 308 had single primary disease. TaqMan real-time polymerase chain reaction was used to measure the exact copy number of GSTM1 and direct sequencing was used to determine genetic variants in the GSTM1 promoter region. Multivariate Cox regression analysis was performed to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) associated with copy number and genetic variants. REC/SPT-free survival times were compared by constructing Kaplan-Meier curves and differences between curves were tested by logrank test. Results showed a significantly decreased REC/SPT (HR = 0.57; 95% CI = 0.35-0.95) and longer REC/SPT-free survival in subjects with at least two copies of GSTM1 compared with the GSTM1 homozygous deletion, but not in those with one copy of GSTM1. The -498G, -426G, and -339T alleles were significantly associated with REC/SPT, with HRs of 0.11 (0.02-0.85), 0.28 (0.11-0.74) and 2.02 (1.07-3.82), respectively. Kaplan-Meier survival analysis showed that the -498G, -426G, and -339C alleles were also significantly associated with increased REC/SPT-free survival. Further haplotype analysis showed the haplotype P(-498G--426G--339C) carriers had decreased REC/SPT with a HR of 0.09 (95% CI 0.01-0.71) and increased REC/SPT-free survival compared with those with haplotype P(-498C--426A--339T). The P(-498C--426A--339T)-containing reporter construct had significantly increased luciferase expression. These results suggest that the GSTM1 CNV and promoter haplotype are better predictors of REC/SPTs of head and neck cancer than just measuring the presence/absence of GSTM1.

Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success.

2-amino-1-methyl-6-phenylimidazo(4,5-b)pyridine (PhIP), a heterocyclic aromatic amine (HCA) formed in meat that is cooked at high temperatures and then ingested, can potentially be retained in human adipose tissues.

Neuroblastoma (NB) arises from the embryonic neural crest and is the most common extracranial solid tumor in children under 5 years of age. Reduced expression of Dicer1 has recently been shown to be in correlation with poor prognosis in NB patients. This study aimed to investigate the mechanisms that could lead to the down-regulation of Dicer1 in neuroblastoma. We used computational prediction to identify potential miRs down-regulating Dicer1 in neuroblastoma. One of the miRs that were predicted to target Dicer1 was miR-192. We measured the levels of miR-192 in 43 primary tumors using real time PCR. Following the silencing of miR-192, the levels of dicer1 cell viability, cell proliferation and migration capability were analyzed. Multivariate analysis identified miR-192 as an independent prognostic marker for relapse in neuroblastoma patients (p=0.04). We were able to show through a dual luciferase assay and side-directed mutational analysis that miR-192 directly binds the 3' UTR of Dicer1 on positions 1232-1238 and 2282-2288. An increase in cell viability, proliferation and migration rates were evident in NB cells transfected with miR-192-mimic. Yet, there was a significant decrease in proliferation when NB cells were transfected with an miR-192-inhibitor We suggest that miR-192 might be a key player in NB by regulating Dicer1 expression.

Using RefSeq annotations, most disease/trait-associated genetic variants identified by genome-wide association studies (GWAS) appear to be located within intronic or intergenic regions, which makes it difficult to interpret their functions. We reassessed GWAS-Associated single-nucleotide polymorphisms (herein termed as GASs) for their potential functionalities using integrative approaches. 8834 of 9184 RefSeq "noncoding" GASs were reassessed to have potential regulatory functionalities. As examples, 3 variants (rs3130320, rs3806932 and rs6890853) were shown to have regulatory properties in HepG2, A549 and 293T cells. Except rs3130320 as a known expression quantitative trait loci (eQTL), rs3806932 and rs6890853 were not reported as eQTLs in previous reports. 1999 of 9184 "noncoding" GASs were re-annotated to the promoters or intragenic regions using Ensembl, UCSC and AceView gene annotations but they were not annotated into corresponding regions in RefSeq database. Moreover, these GAS-harboring genes were broadly expressed across different tissues and a portion of them was expressed in a tissue-specific manner, suggesting that they could be functional. Collectively, our study demonstrates the benefits of using integrative analyses to interpret genetic variants and may help to predict or explain disease susceptibility more accurately and comprehensively.

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify types of cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and cross-validated resource for epigenetics research from the FDA's Epigenomics Quality Control Group.